In today's world, motor vehicles have become a primary mode of commuting. With each passing day, these vehicles are becoming smart and aware about their surroundings and environment. Today a typical motor vehicle may be equipped with features like navigation assist, parking assist, and so forth. In coming times, the motor vehicles will become smarter, more intelligent, and autonomous. For example, autonomous vehicles may be capable of navigating without manual intervention, thereby facilitating efficient and effective transportation.
These smart or autonomous vehicles may be capable of sensing the dynamic changing environment, detecting obstacles on a navigation path, automatically controlling speed, automatically breaking, autonomous navigation, performing vehicle diagnostic analysis, and so forth. However, these features would require a lot of computing power by the vehicle control system so as to quickly process enormous data and make it readily available for the vehicle control system for decision making.
One of the requirement to enable some of the above mentioned features is to extract the road features such as width of road, humps present in the road, number of road intersection, angle of turn at intersection, slope of the road, etc. As will be appreciated, extracting these important road features needs more computing power and expensive hard wares such as light detection and ranging (LIDAR) scanner etc. in each of the vehicles. Currently available navigation maps (e.g., GOOGLE maps) do not provide information on above mentioned road features, and dynamically extracting such information while a vehicle is running using sensors installed on the vehicle becomes expensive and computationally intensive. In some cases, the dynamic generation of navigation map and extraction road features may also require continuous communicative connection with a central server, and may fail when the connection is interrupted.